This section is intended to introduce the reader to various aspects of art, which may be associated with embodiments of the present invention. This discussion is believed to be helpful in providing the reader with information to facilitate a better understanding of particular techniques of the present invention. Accordingly, it should be understood that these statements are to be read in this light, and not necessarily as admissions of prior art.
The oil and gas industry incurs substantial operating costs to drill wells in the exploration and development of hydrocarbon resources. The cost of drilling wells may be considered to be a function of time due to the equipment and manpower expenses based on time. The drilling time can be minimized in at least two ways: 1) maximizing the Rate-of-Penetration (ROP) (i.e., the rate at which a drill bit penetrates the earth); and 2) minimizing the non-drilling rig time (e.g., time spent on tripping equipment to replace or repair equipment, constructing the well during drilling, such as to install casing, and/or performing other treatments on the well). Past efforts have attempted to address each of these approaches. For example, drilling equipment is constantly evolving to improve both the longevity of the equipment and the effectiveness of the equipment at promoting a higher ROP. Moreover, various efforts have been made to model and/or control drilling operations to avoid equipment-damaging and/or ROP limiting conditions, such as vibrations, bit-balling, etc.
Many attempts to reduce the costs of drilling operations have focused on increasing ROP. For example, U.S. Pat. Nos. 6,026,912; 6,293,356; and 6,382,331 each provide models and equations for use in increasing the ROP. In the methods disclosed in these patents, the operator collects data regarding a drilling operation and identifies a single control variable that can be varied to increase the rate of penetration. In most examples, the control variable is Weight On Bit (WOB); the relationship between WOB and ROP is modeled; and the WOB is varied to increase the ROP. While these methods may result in an increased ROP at a given point in time, this specific parametric change may not be in the best interest of the overall drilling performance in all circumstances. For example, bit failure and/or other mechanical problems may result from the increased WOB and/or ROP. While an increased ROP can drill further and faster during the active drilling, delays introduced by damaged equipment and equipment trips required to replace and/or repair the equipment can lead to a significantly slower overall drilling performance. Furthermore, other parametric changes, such as a change in the rate of rotation of the drillstring (RPM), may be more advantageous and lead to better drilling performance than simply optimizing along a single variable.
Because drilling performance is measured by more than just the instantaneous ROP, methods such as those discussed in the above-mentioned patents are inherently limited. Other research has shown that drilling rates can be improved by considering the Mechanical Specific Energy (MSE) of the drilling operation and designing a drilling operation that will minimize MSE. For example, U.S. Pat. Nos. 7,857,047, and 7,896,105, each of which is incorporated herein by reference in their entirety for all purposes, disclose methods of calculating and/or monitoring MSE for use in efforts to increase ROP. Specifically, the MSE of the drilling operation over time is used to identify the drilling condition limiting the ROP, often referred to as a “founder limiter”. Once the founder limiter has been identified, one or more drilling variables can be changed to overcome the founder limiter and increase the ROP. As one example, the MSE pattern may indicate that bit-balling is limiting the ROP. Various measures may then be taken to clear the cuttings from the bit and improve the ROP, either during the ongoing drilling operation or by tripping and changing equipment.
Recently, additional interest has been generated in utilizing artificial neural networks to optimize the drilling operations, for example U.S. Pat. No. 6,732,052, U.S. Pat. No. 7,142,986, and U.S. Pat. No. 7,172,037. However the limitations of neural network based approaches constrain their further application. For instance, the result accuracy is sensitive to the quality of the training dataset and network structures. Neural network based optimization is limited to local search and has difficulty in processing new or highly variable patterns.
In another example, U.S. Pat. No. 5,842,149 disclosed a close-loop drilling system intended to automatically adjust drilling parameters. However, this system requires a lookup table to provide the relations between ROP and drilling parameters. Therefore, the optimization results depend on the effectiveness of this table and the methods used to generate this data, and consequently, the system may lack adaptability to drilling conditions which are not included in the table. Another limitation is that downhole data is required to perform the optimization.
While these past approaches have provided some improvements to drilling operations, further advances and more adaptable approaches are still needed as hydrocarbon resources are pursued in reservoirs that are harder to reach and as drilling costs continue to increase. Further desired improvements may include expanding the optimization efforts from increasing ROP to optimizing the drilling performance measured by a combination of factors, such as ROP, efficiency, downhole dysfunctions, etc. Additional improvements may include expanding the optimization efforts from iterative control of a single control variable to control of multiple control variables. Moreover, improvements may include developing systems and methods capable of recommending operational changes during ongoing drilling operations.
While such research objectives can be readily appreciated when considered in this light, U.S. Patent Publications 2012/0118637 and 2012/0123756 disclose a data-driven based advisory system. The advisory system uses a PCA (principal component analysis) method to compute the correlations between controllable drilling parameters and an objective function. This objective function can be either a single-variable based performance measurement (MSE, ROP, DOC, or bit friction factor mu) or a mathematical combination of MSE, ROP, and other performance variables such as vibration measurement. Since PCA is based on a local search of a subset of the relevant data in a window of interest (the window can be over an interval of formation depth or over time), the searched results may become trapped at local optimum points (sometimes called stationary points). Therefore, need exists to integrate local search methods such as PCA with global search methods to mitigate this issue. (Global searches are performed on the entire window of relevant data, whereas local searches are performed on subsets of the windowed data.)
Some prior disclosures taught systems and methods that may be generally summarized by the following steps: 1) receiving data regarding drilling parameters wherein one, two, or more of the drilling parameters are controllable; 2) utilizing a statistical model to identify one, two, or more controllable drilling parameters having significant correlation to either an objective function incorporating two or more drilling performance measurements or some other drilling performance measurement; 3) generating operational recommendations for one, two, or more controllable drilling parameters, wherein the operational recommendations are selected to optimize the objective function or the drilling performance measurement, respectively; 4) determining operational updates to at least one controllable drilling parameter based at least in part on the generated operational recommendations; and 5) implementing at least one of the determined operational updates in the ongoing drilling operations.
As wellbore drilling operations progress through an earthen formation, the drill bit axially advances through the formation at a measured rate of penetration, which is commonly calculated as the measured depth drilled over time. As the formation conditions depend on location, depth, and even time, the drilling conditions necessarily change over time and range within a given wellbore or other formation bore. Moreover, the drilling conditions may change in manners that dramatically reduce the efficiencies of the drilling operation and/or that create less preferred operating conditions. Accordingly, research is continually seeking improved methods of predicting and detecting changes in drilling conditions. Some aspects of past research have focused on “local” search based optimization schemes such as neural networks or statistical methods. Since the searched results may be trapped at local optimum points (also called stationary points), these algorithms may not always provide the best solution over a range of drilling depth or time. On the other hand, some empirical methods also have been used to find the “best” drilling parameters within a data window but such methods still cannot determine which direction to change a parameter to find a new set of optimized parameters that will perform better than the previously used parameters.
The presently disclosed and claimed systems and methods provide improvements over these previous paradigms and short-comings. The prior art methods and systems could be further improved by implementing a revised approach for determining whether the data used to make predictions is quality data of flawed data. It is desired to have improved data for which to make operational parameter optimization determinations.